CN114936524A

CN114936524A - Storage battery internal resistance identification method, storage battery internal resistance identification device, storage battery internal resistance identification equipment, storage battery internal resistance identification medium and program product

Info

Publication number: CN114936524A
Application number: CN202210588377.8A
Authority: CN
Inventors: 兰竣杰; 魏金林; 黄大彬; 汪子腾; 张�浩; 杨博; 李谱; 刘诺舟; 蒋朋; 李仕凯; 张函; 郭康; 颜帅; 吴镇宇; 徐家将; 吕星岐; 魏国富; 毛文俊
Original assignee: Kunming Bureau of Extra High Voltage Power Transmission Co
Current assignee: Kunming Bureau of Extra High Voltage Power Transmission Co
Priority date: 2022-05-27
Filing date: 2022-05-27
Publication date: 2022-08-23

Abstract

The application relates to a method, a device, equipment, a medium and a program product for identifying internal resistance of a storage battery. The method comprises the following steps: establishing an equivalent circuit model of the storage battery to obtain a plurality of internal resistance parameters to be identified; determining a fitness function and a constraint condition; the fitness function comprises a deviation relation between a measured value and a calculated value of the internal resistance parameter, and the constraint condition comprises a variation range of each internal resistance parameter; and solving the plurality of internal resistance parameters according to a peacock optimization algorithm, the fitness function and the constraint condition to obtain an optimal solution of the plurality of internal resistance parameters. By adopting the method, more accurate internal resistance parameters can be obtained.

Description

Storage battery internal resistance identification method, storage battery internal resistance identification device, storage battery internal resistance identification equipment, storage battery internal resistance identification medium and program product

Technical Field

The present application relates to the field of electrical technologies, and in particular, to a method, an apparatus, a device, a medium, and a program product for identifying internal resistance of a battery.

Background

The storage battery is used as an important component in a low-voltage direct-current system, the health condition of the storage battery plays an important role in the safe operation of a power plant and a transformer substation, and the storage battery is used for guaranteeing the safe and stable operation of a power grid.

Theoretically, the internal resistance of the storage battery can be measured through an RC network model and a heuristic algorithm. However, the practical application of the RC network model is difficult, and the heuristic algorithm cannot solve the complex highly nonlinear and non-convex problems well.

Therefore, how to accurately distinguish the internal resistance of the storage battery is important.

Disclosure of Invention

In view of the above, it is necessary to provide a battery internal resistance identification method, apparatus, device, medium, and program product capable of accurately discriminating the internal resistance of a battery in view of the above technical problems.

In a first aspect, the application provides a storage battery internal resistance identification method. The method comprises the following steps:

establishing an equivalent circuit model of the storage battery to obtain a plurality of internal resistance parameters to be identified;

determining a fitness function and a constraint condition; the fitness function comprises a deviation relation between a measured value and a calculated value of the internal resistance parameter, and the constraint condition comprises a variation range of each internal resistance parameter;

and solving the plurality of internal resistance parameters according to the peacock optimization algorithm, the fitness function and the constraint condition to obtain the optimal solution of the plurality of internal resistance parameters.

In a second aspect, the application further provides a storage battery internal resistance recognition device. The device comprises:

the model establishing module is used for establishing an equivalent circuit model of the storage battery to obtain a plurality of internal resistance parameters to be identified;

the function determining module is used for determining a fitness function and a constraint condition; the fitness function comprises a deviation relation between a measured value and a calculated value of the internal resistance parameter, and the constraint condition comprises a variation range of each internal resistance parameter;

and the parameter solving module is used for solving the plurality of internal resistance parameters according to the peacock optimization algorithm, the fitness function and the constraint condition to obtain the optimal solution of the plurality of internal resistance parameters.

In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the first aspect when executing the computer program.

In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the first aspect.

In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program that when executed by a processor performs the steps of the first aspect.

The storage battery internal resistance identification method, the storage battery internal resistance identification device, the storage battery internal resistance identification equipment, the storage battery internal resistance identification medium and the storage battery internal resistance identification program product establish an equivalent circuit model of the storage battery to obtain a plurality of internal resistance parameters to be identified; determining a fitness function and a constraint condition; and solving the plurality of internal resistance parameters according to the peacock optimization algorithm, the fitness function and the constraint condition to obtain the optimal solution of the plurality of internal resistance parameters. Therefore, the accurate internal resistance parameters can be obtained by adopting the method, operation and maintenance personnel in the transformer substation and the converter station can find the storage batteries with reduced performance in time according to the internal resistance parameters of the storage batteries, and can maintain or replace the storage batteries in time, so that serious operation accidents of a power grid are avoided.

Drawings

FIG. 1 is a schematic flow chart of a method for identifying internal resistance of a storage battery in one embodiment;

FIG. 2 is a schematic flow chart illustrating a step of solving a plurality of internal resistance parameters according to a peacock optimization algorithm, a fitness function, and constraints under an embodiment;

FIG. 3 is a schematic flowchart illustrating a step of performing multiple iterative computations on multiple internal resistance parameters according to roles, fitness functions, and constraints in one embodiment;

FIG. 4 is a schematic flow chart illustrating the steps for updating the location of each peacock based on the adaptive behavior of each peacock in one embodiment;

FIG. 5 is a schematic flow chart illustrating a process for updating the position of each female peacock by female peacock self-adapting to approach male peacock behavior in one embodiment;

FIG. 6 is a schematic diagram of the flow of the process of updating the positions of the young peacocks through the adaptive search behavior of the young peacocks in one embodiment;

FIG. 7 is a schematic flow chart of a process for updating the position of a second male peacock, in addition to a first male peacock, by the action of interaction between the male peacocks, under one embodiment;

FIG. 8 is a flowchart illustrating the steps for assigning a role to each peacock based on fitness in one embodiment;

FIG. 9 is a schematic flow chart showing a process of identifying the internal resistance of the secondary battery in another embodiment;

FIG. 10 is a block diagram showing the structure of a device for identifying the internal resistance of a secondary battery in one embodiment;

FIG. 11 is a second block diagram illustrating the structure of the device for identifying the internal resistance of a battery according to the embodiment;

FIG. 12 is a diagram illustrating an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

In one embodiment, as shown in fig. 1, a method for identifying internal resistance of a storage battery is provided, and this embodiment of the present application is exemplified by applying the method to a server. The embodiment of the application can comprise the following steps:

s101, establishing an equivalent circuit model of the storage battery to obtain a plurality of internal resistance parameters to be identified.

The equivalent circuit model of the storage battery is a three-order Thevenin equivalent circuit model, the model comprises an ideal voltage source, a resistor and three RC rings, and the three RC rings comprise ohmic polarization resistors R ₁ And ohmic polarization capacitance C ₁ The RC ring is formed by electrochemical polarization internal resistance R ₂ And electrochemicalChemical polarization capacitance C ₂ One RC ring polarizes the internal resistance R by the concentration difference ₃ Sum concentration difference polarization capacitance C ₃ And (4) forming. The equivalent circuit model can be described by the mathematical formula (1) and formula (2) as:

in the formula, Δ t represents a sampling period, u _n (n-1, 2,3) represents the voltage across the three RC rings, respectively, with time constant τ _n ＝R _n C _n (n＝1,2,3)，u _oc Is the open circuit voltage of the battery u _L To calculate the value voltage, i _L Is the model current. The parameter to be identified has u _oc ，R ₀ ，R ₁ ，R ₂ ，R ₃ ，C ₁ ，C ₂ And C ₃ 。

A user can establish the equivalent circuit model by using the MATLAB software and adopting the programming grammar corresponding to the MATLAB software.

And S102, determining a fitness function and a constraint condition.

The fitness function comprises a deviation relation between a measured value and a calculated value of the internal resistance parameter, and the constraint condition comprises a variation range of each internal resistance parameter.

The measured value of the internal resistance parameter is acquired through a constant-current voltage-limiting charging and checking discharge test. The constant-current voltage-limiting charging refers to charging in a constant-current mode, and when the voltage of the storage battery pack rises to a voltage-limiting value, the charging device automatically converts the voltage-limiting charging into the voltage-limiting charging until the charging is finished. The check discharge test is a process of artificially evaluating the charge condition of the storage battery by constant current discharge under the conditions of a specified discharge temperature, a discharge current and a discharge termination voltage.

The fitness function can be a root mean square error between the measured value and the calculated value of the internal resistance parameter, and the mathematical expression of the fitness function is formula (3):

in the formula, K is the number of data points searched by the peacock optimization algorithm,

as a voltage measurement value u _L Calculated as a voltage.

The constraint condition is a parameter u to be identified in an equivalent circuit model of the storage battery _oc ，R ₀ ，R ₁ ，R ₂ ，R ₃ ，C ₁ ，C ₂ And C ₃ The actual value range of (a), that is, the search range of the peacock optimization algorithm, can be expressed as formula (4) in a mathematical form:

a user can determine a voltage calculation value u by combining a peacock optimization algorithm and a formula (2) _L Acquiring measured value voltage measured value through constant current voltage-limiting charging and checking discharge test

The value of the fitness function is thus calculated with the aid of MATLAB software according to equation (3). Furthermore, the constraint conditions may also be set by MATLAB software.

S103, solving the plurality of internal resistance parameters according to the peacock optimization algorithm, the fitness function and the constraint conditions to obtain the optimal solution of the plurality of internal resistance parameters.

The peacock optimization algorithm simulates the hierarchical structures of adult male peacocks, adult female peacocks and young peacocks and the dynamic group behaviors of the peacocks in the foraging process to gradually approach the optimal solution of the problem. In particular, the food search and the unique rotary dance behaviors of peacocks are utilized, and the self-adaptive search and approach mechanisms of peacocks and peacocks are utilized.

According to the peacock optimization algorithm, the peacocks are enabled to realize multiple iterations from the initial positions, the value of the fitness function can be calculated according to the iteration result each time, and when the value of the fitness function meets the preset condition, the peacocks reach the position of the optimal solution, namely the optimal solution of the internal resistance parameter.

In the storage battery internal resistance identification method, an equivalent circuit model of the storage battery is established to obtain a plurality of internal resistance parameters to be identified; determining a fitness function and a constraint condition; and solving the plurality of internal resistance parameters according to the peacock optimization algorithm, the fitness function and the constraint conditions to obtain an optimal solution of the plurality of internal resistance parameters. In the embodiment of the application, because the fitness function and the constraint condition are used, the fitness function plays a role in measuring whether the solution of the internal resistance parameters is the optimal solution, the constraint condition limits the search position of the peacock optimization algorithm, the peacock optimization algorithm utilizes food search and unique rotary dance behaviors of peacocks and self-adaptive search and approach mechanisms of the peacocks and the peacocks, so that the optimal solutions of a plurality of internal group parameters can be more accurately obtained, the internal resistance parameters are more accurate, operation and maintenance personnel in a transformer substation and a convertor station can find the storage batteries with reduced performance in time according to the internal resistance parameters of the storage batteries, and the storage batteries can be maintained or replaced in time, and the power grid is prevented from generating serious operation accidents.

In an embodiment, as shown in fig. 2, the step of solving the plurality of internal resistance parameters according to the peacock optimization algorithm, the fitness function, and the constraint condition to obtain an optimal solution of the plurality of internal resistance parameters may include:

s201, initializing the position and fitness according to a peacock optimization algorithm.

The position of each peacock corresponds to a group of solutions of the internal resistance parameters, and the fitness of each peacock represents the deviation degree between the group of solutions of the internal resistance parameters and the measured value.

The position of one peacock can be expressed as a group of solutions X ═ u _oc ，R ₀ ，R ₁ ，R ₂ ，R ₃ ，C ₁ ，C ₂ ，C ₃ ). For example of the initial peacockIf the population number is set as 401, 401 peacocks are represented, and the position of 401 peacocks corresponds to the solution X of the 401 group of the internal resistance parameter (u-is _oc ，R ₀ ，R ₁ ，R ₂ ，R ₃ ，C ₁ ，C ₂ ，C ₃ )。

According to the peacock optimization algorithm, the initial position of each peacock can be set as a search space, specifically, the search space is an 8-dimensional space formed by the value range of the formula (4), and the initial position of each peacock is a random position in the 8-dimensional space. The fitness is initialized as a result of the calculation of this random position by equation (3).

And S202, allocating roles to each peacock according to the fitness.

Wherein the characters include male peacocks, female peacocks, and young peacocks.

And all peacocks are sorted according to the fitness of the peacocks, so that role distribution is performed. For example, the first 5 peacocks with higher fitness are male peacocks, and of the peacocks other than the male peacocks, the first 30% of the peacocks with higher fitness are determined as female peacocks, and the remaining peacocks are determined as young peacocks.

And allocating each peacock as a male peacock, a female peacock and a young peacock according to the initialized fitness.

And S203, carrying out iterative calculation on the plurality of internal resistance parameters for a plurality of times according to the roles, the fitness function and the constraint conditions to obtain an optimal solution of the plurality of internal resistance parameters.

Wherein the optimal solution of the internal resistance parameter refers to a solution when iteration of the peacock optimization algorithm is finished.

According to three roles of the male peacock, the female peacock and the young peacock, different roles correspond to different position updating mechanisms, for example, the position updating mechanism under the duality behavior of the male peacock corresponding to the male peacock and the interaction behavior between the male peacocks, the position updating mechanism under the behavior of the male peacock is close to the self-adaptation of the female peacock corresponding to the female peacock, and the position updating mechanism under the self-adaptation searching behavior of the young peacock corresponding to the young peacock. The role adopts a corresponding position updating mechanism to update the position under the constraint condition, and after iterative computation of multiple position updating, each iterative computation can be carried outObtaining a fitness function value, and obtaining 8 internal resistance parameters u when the fitness function value is smaller than a preset value and meets a preset condition _oc ，R ₀ ，R ₁ ，R ₂ ，R ₃ ，C ₁ ，C ₂ ，C ₃ The optimal solution of (1).

In the embodiment, the position and the fitness are initialized according to a peacock optimization algorithm; allocating roles to each peacock according to the fitness; and carrying out repeated iterative calculation on the plurality of internal resistance parameters according to the roles, the fitness function and the constraint conditions to obtain the optimal solution of the plurality of internal resistance parameters. According to the method and the device, the value with the fitness smaller than the preset value and in accordance with the preset condition is searched according to different position updating mechanisms corresponding to different roles, the value is used as the optimal solution, the situation that the prior algorithm is trapped in local optimization, so that the optimal solution is a fake solution is avoided, the global optimization capability is improved, and the method and the device become effective tools for solving the non-convex problem.

In an embodiment, as shown in fig. 3, the step of performing multiple iterative computations on the multiple internal resistance parameters according to the role, the fitness function, and the constraint condition to obtain an optimal solution of the multiple internal resistance parameters may include:

s301, in the process of each iterative computation, the positions of the peacocks are updated based on the self-adaptive behaviors of the peacocks.

The self-adaptive behaviors of the peacocks comprise the duality behavior of the male peacocks, the self-adaptive approaching behavior of the female peacocks to the male peacocks, the self-adaptive searching behavior of the young peacocks and the interaction behavior between the male peacocks.

And in each iterative calculation process, updating the positions of the peacocks by adopting a corresponding position updating mechanism based on the self-adaptive behaviors corresponding to the peacocks.

And S302, recalculating the fitness according to the updated positions of the peacocks.

And the updated positions of the peacocks refer to positions obtained by calculating the peacocks again based on a peacock optimization algorithm after each iteration.

Substituting the updated positions of the peacocks into the formula (1) and the formula (2) of the equivalent model circuit to calculate the voltmeterCalculated value u _L And then substituting the fitness function formula (3) to obtain a new fitness value of each peacock. The better the fitness of each peacock, the more accurate the internal resistance parameter of the storage battery to be distinguished.

And S303, reallocating roles according to the recalculated fitness, and performing iterative computation again according to the reallocating roles.

And according to the recalculated fitness, carrying out role division on each peacock again. The N peacocks with high fitness are distributed as male peacocks, the non-male peacocks with high fitness and a preset proportion are distributed as female peacocks, and the rest peacocks are distributed as young peacocks.

And after the roles are redistributed, the positions of the peacocks are renewed based on the self-adaptive behaviors of the peacocks, the new fitness is calculated according to the renewed positions of the peacocks, and the calculation is repeated and repeated continuously.

And S304, obtaining the optimal solution of a plurality of internal resistance parameters according to the final position of each peacock after the iteration is finished.

Wherein, the end condition of the iteration comprises: reaching the preset maximum iteration times. For example, the maximum number of iterations may be set to 4020. The maximum iteration number is not limited, and can be set according to actual conditions.

And when the iteration is finished, the corresponding fitness of the final position of each peacock is smaller than a preset value, and the positions meet preset conditions, namely the optimal solution of the internal resistance parameters.

In the embodiment, in each iterative computation process, the positions of the peacocks are updated based on the self-adaptive behaviors of the peacocks, and the self-adaptive behaviors of the peacocks are different, so that corresponding position updating mechanisms are different, and the different position updating mechanisms ensure that the peacock optimization algorithm can effectively search an ideal search area and position a global optimal solution; in the process, the value of the fitness is replaced only when the fitness after iteration is better than the fitness before iteration, so that the peacock optimization algorithm can be guaranteed to finally realize convergence.

In an embodiment, as shown in fig. 4, the step of updating the position of each peacock based on the adaptive behavior of each peacock may include:

s401, updating the positions of the female peacocks by the self-adaption of the female peacocks approaching to the behavior of the male peacocks.

The self-adaptive approaching behavior of the female peacock means that the female peacock is influenced by the duality behavior of the male peacock, gradually approaches to the male peacock, and the self position is dynamically adjusted by observing the periphery. Here, the more suitable the male peacock is, the greater the probability that the female peacock is attracted by the male peacock is.

The positions of the male peacocks are searched under the constraint condition through a position updating mechanism under the action of the male peacocks for seeking a couple, specifically, the positions of the male peacocks are searched in a surrounding search space mainly through a rotation-dance mechanism, the female peacocks are influenced by the position updating mechanism of the male peacocks, and the updated positions are adjusted according to the position updating mechanism corresponding to the action that the self-adaption of the female peacocks is close to the male peacocks.

S402, updating the positions of the young peacocks through the self-adaptive search behavior of the young peacocks.

The self-adaptive search behavior of the young peacocks means that the young peacocks randomly select and move one male peacock to the young peacocks, and meanwhile, random search is conducted under the constraint condition by means of a Levy flight mechanism. The Levy flight mechanism is a random walk, can effectively explore a search space, and has a good prospect in the aspect of searching for a global optimal solution. The method adds the Levy flight to the peacock optimization algorithm to improve the optimization performance of the peacock optimization algorithm.

The position updating mechanism of the young peacocks is influenced by the iteration period of the peacock optimization algorithm. In the initial stage of iteration of a peacock optimization algorithm, the young peacocks are mainly randomly searched under the constraint condition; in the middle and later stages of the iteration of the peacock optimization algorithm, the young peacocks gradually converge towards the male peacocks, and the male peacocks are generally 5 male peacocks with the best fitness.

And the young peacocks adjust the updating positions through the position updating mechanism.

S403, updating the positions of second male peacocks except the first male peacocks through the interaction behavior among the male peacocks; the first male peacock is the one with the highest fitness.

Wherein a first male peacock has the best food source and is considered as a leader, and a second male peacock other than the first male peacock is guided by the first male peacock to move toward it gradually. In particular, the second male peacocks other than the first male peacocks do not move directly toward the first male peacocks, but move randomly toward the first male peacocks within a certain range from the first male peacocks.

In the iterative process of the peacock optimization algorithm, the male peacocks have interaction behaviors, and the second male peacocks except the first male peacocks randomly move to the first male peacocks within a certain range to update the positions.

In the above embodiment, the positions of the female peacocks are updated by the self-adaptation of the female peacocks close to the male peacocks, the positions of the child peacocks are updated by the self-adaptation search of the child peacocks, the positions of the male peacocks of the second other than the first male peacocks are updated by the interaction between the male peacocks, and the first male peacocks are smaller than the preset value for the degree of adaptation and meet the preset condition. After the self-adaptation behaviors corresponding to the male peacocks, the female peacocks and the young peacocks are executed, the positions of the corresponding peacocks can be replaced and updated only when the fitness of the peacocks becomes better, and convergence of a peacock optimization algorithm can be ensured on the basis.

In one embodiment, as shown in fig. 5, the step of updating the position of each female peacock through the self-adaptive approach of the female peacock to the male peacock may include:

s4011, obtaining the position of the female peacock, the current position of the male peacock, a first random number, a first position updating operator and a first searching operator after iteration.

Obtaining parameters in a female peacock behavior description formula, and iterating the position x of the female peacock _h Current position x of the female peacock _h (t), current position x of the male peacock _c,n A first random number r ₅ The system comprises a first position updating operator A and a first search operator theta.

S4012, calculating the positions of the iterative female peacocks according to a preset female peacock behavior description formula, the positions of the iterative female peacocks, the current position of the male peacocks, a first random number, a first position update operator and a first search operator.

Wherein, the description formula of the preset female peacock behavior is as follows:

x _h ＝x _h (t)+3·θ·(x _c,n -x _h (t)), if r ₅ ∈A (5)

In the formula, x _h For the position of the female peacock after iteration, x _h (t) is the current position of the female peacock, and h is the number of the female peacocks; x is the number of _c,n Is the position of the current male peacock, and n is 1,2,3,4, 5; r is ₅ Is [0,1 ]]A first random number within a range; a is a first position updating operator for determining the position updating of the female peacock, and when n is equal to 1,2,3,4 and 5, A is [0.6,1 respectively]，[0.4,0.6]，[0.2,0.4]，[0.1,0.2]，[0,0.1]Specifically, the following formula can be expressed:

theta is the first search operator used to balance the local search and global search for a female peacock. Theta ₀ And theta ₁ Are set to 0.1 and 1, K and K, respectively _max The current iteration times and the maximum iteration times are respectively calculated as follows:

θ＝θ ₀ +(θ ₁ -θ ₀ )·(K/K _max ) (7)

under this mechanism, when theta<1/3 hours (initial period of iteration), position x of femtos after iteration _h And x _h (t) position of the current female peacock is almost the same, but position x of the female peacock after iteration _h Or predominantly towards the selected position of the male peacock, representing the local survey process of the female peacock; when theta is>1/3 (middle and later iteration), position x of femtos after iteration _h And x _h (t) the current position of the female peacock is greatly different, and the female peacock tends to move relative to the selected male peacock, representing the female peacockAnd (4) carrying out a global search process on the sparrows.

The positions x of the female peacocks after iteration _h Current position x of the female peacock _h (t), current position x of male peacock _c,n A first random number r ₅ The first position updating operator A and the first search operator theta are substituted into a preset female peacock behavior description formula (5), and the position x of the female peacock after iteration is calculated _h 。

In the above embodiment, the positions of the female peacocks after iteration, the current position of the female peacock, the current position of the male peacock, the first random number, the first position update operator, and the first search operator are obtained; and calculating the positions of the iterative female peacocks according to a preset female peacock behavior description formula, the positions of the iterative female peacocks, the current positions of the male peacocks, a first random number, a first position update operator and a first search operator. In the iteration process, the ratio of the current iteration times to the maximum iteration times is gradually increased, so that the self-adaptive adjustment of a search operator is caused, the female peacocks are focused on local development in the initial stage of iteration, and tend to emphasize global exploration in the middle and later stages, which is beneficial to finding out a more accurate optimal solution.

In an embodiment, as shown in fig. 6, the step of updating the positions of the young peacocks through the adaptive search behavior of the young peacocks may include:

s4021, acquiring the position of a young peacock, the position of a current young peacock, a flight factor, the position of a male peacock followed by the young peacock, the position of the current male peacock, a second random number, a second position updating operator and a dynamic change operator after iteration.

Obtaining parameters in a young peacock behavior description formula, and iterating the position x of the young peacock _cu Position x of the current young peacock _cu (t), flight factor Levy, male peacock position x followed by young peacocks _pu Position x of the current male peacock _c,n (t), a second random number r ₆ Second position update operator B, dynamic change operators alpha and beta.

S4022, calculating the positions of the iterative young peacocks according to a preset young peacock behavior description formula, the positions of the iterative young peacocks, the position of the current young peacocks, a flight factor, the position of the male peacock followed by the young peacocks, the position of the current male peacock, a second random number, a second position update operator and a dynamic change operator.

Wherein, the preset young peacock behavior description formula is as follows:

in the formula, x _cu Position of the young peacock after iteration, x _cu (t) is the current position of the young peacock; levy is a flight factor and can be set to a fixed value; x is the number of _pu The position of a male peacock followed by a young peacock; x is the number of _c,n (t) is the current position of the male peacock, n is 1,2,3,4, 5; r is ₆ Is [0,1 ]]A second random number within the range; b is a second position update operator, and when n is 1,2,3,4,5, B is [0.8,1, respectively],[0.6,0.8],[0.4,0.6],[0.2,0.4],[0,0.2](ii) a Specifically, the following formula can be expressed:

α and β are both dynamic change operators, defined as follows:

in the formula, alpha ₀ ，α ₁ ，β ₀ And beta ₁ Set to 0.9, 0.4, 0.1 and 1, K and K _max Respectively the current iteration number and the maximum iteration number.

Under the mechanism, when alpha is larger than beta (at the initial stage of iteration), the formula (8) is greatly influenced by Levy flight factors, and young peacocks are mainly randomly searched; when beta is larger than alpha (in the middle and later period of iteration), the position x of the male peacock followed by the young peacock is shown in the formula (8) _pu The influence is large, and x _pu Then according to the current position x of the male peacock _c,n (t) determine, therefore, the young peacock gradually converged towards 5 male peacocks.

Position x of the young peacock after iteration _cu Position x of the current young peacock _cu (t), flight factor Levy, male peacock position x followed by young peacocks _pu Current position of the male peacock x _c,n (t), a second random number r ₆ Updating the operator B at the second position, substituting the dynamic change operators alpha and beta into a preset young peacock behavior description formula (8), and calculating the position x of the young peacock after iteration _cu 。

In the above embodiment, the position of the young peacock, the position of the current young peacock, the flight factor, the position of the male peacock followed by the young peacock, the position of the current male peacock, the second random number, the second position update operator, and the dynamic change operator after iteration are obtained; and calculating the position of the young peacock after iteration according to a preset young peacock behavior description formula, the position of the young peacock after iteration, the position of the current young peacock, a flight factor, the position of the male peacock followed by the young peacock, the position of the current male peacock, a second random number, a second position updating operator and a dynamic change operator. In the iteration process, the ratio of the current iteration times to the maximum iteration times is gradually increased, so that the self-adaptive adjustment of a dynamic change operator is caused, therefore, the young peacocks are focused on local development in the initial stage of iteration, the global exploration is more apt to be emphasized in the middle and later stages, and the search mechanism can be effectively complemented with the search mechanism of the female peacocks. Therefore, local development and global exploration are carried out in the whole iteration process, and the optimization quality, efficiency and stability of the peacock optimization algorithm are improved.

In one embodiment, as shown in fig. 7, the process of updating the position of a second male peacock other than a first male peacock through the interaction between the male peacocks may include:

s4031, a random vector, the position of the current male peacock, a third search operator and a third random number are obtained.

Obtain random vector x' _r,n Current position x of the male peacock _c,1 And x _c,n (t), a third search operator θ and a third random number r _n '。

And S4032, calculating the position of a second male peacock according to a preset male peacock interaction behavior description formula, a random vector, the current position of the male peacock, a third search operator and a third random number.

Wherein a first male peacock is considered a leader since it has the best food source, and a second male peacock other than the first is guided by the first male peacock to move toward it gradually. The second male peacock position updating formula, in addition to the first male peacock, is as follows:

in the formula, x _c,n For a second male peacock position, x, after iteration, in addition to the first male peacock _c,n (t) is a second male peacock location that is currently other than the first male peacock; theta is a third search operator; r is a radical of hydrogen _n ' is [0,1 ]]A third random number within the range; x' _r,n Is a random vector; x is the number of _c,1 Is the current position of the first male peacock; d _n And D _n Is an intermediate variable.

Random vector x' _r,n Current position x of the male peacock _c,n (t) and x _c,1 A third search operator theta and a third random number r _n Substituting into a second male peacock position updating formula (11) except for the first male peacock, and calculating the position x of the second male peacock except for the first male peacock after iteration _c,n 。

In the above embodiment, a random vector, a current position of a male peacock, a third search operator, and a third random number are obtained; and calculating the position of a second male peacock according to a preset male peacock interaction behavior description formula, a random vector, the current position of the male peacock, a third search operator and a third random number. The convergence of the algorithm can be ensured on the basis that the positions of second male peacocks except for the first male peacock are directly or indirectly influenced by the position of the first male peacock through the interaction action among the male peacocks. In addition, other peacocks can not be converged to the peacock I directly, and premature convergence and local optimization can be effectively avoided.

In an embodiment, the updating the position of each peacock based on the adaptive behavior of each peacock may further include: and dynamically adjusting the position of the male peacock based on the duality behavior of the male peacock.

After discovering a food source, the male peacocks can rotate in place or around the food source to attract the attention of the female peacocks, and the position updating mechanism of the male peacocks in the coupling process can be described as follows:

in the formula, x _c,1 Position of the first drone after iteration, x _c,1 (t) is the current first male peacock position; x is the number of _c,n (n-2, 3,4,5) is the position of a second male peacock after iteration, except the first, x _c,n (t) is the current position of a second male peacock other than the first; r is a radical of hydrogen _n Is [0,1 ]]A random number within a range; σ and ∈ are third position update operators, and when n is 2,3,4, and 5, σ/∈ is 1.5/0.9, 2/0.8, 3/0.6, and 5/0.3, respectively, which can be specifically expressed as the following formula (13):

x _r,1 and x _r,n (n-2, 3,4,5) is a random vector; r _s The radius at which the male peacock rotates around the food source can be described as:

x _r ＝2·rand(1,Dim)-1 (14)

where Dim is the number of internal resistance parameters and rand (1, Dim) represents the generation of 1 row and 8 columns of random numbers, i.e., x _r,1 And x _r,n A random vector of (a); k and K _max Respectively the current iteration times and the maximum iteration times; r _s0 Is an initial rotation radius vector; c _v Is a male peacock twiddle factor and is set to be 0.2; x is the number of _ub And x _lb The internal resistance parameters correspond to the upper limit and the lower limit of the constraint condition.

In the above embodiment, the positions of the male peacocks are dynamically adjusted based on the duality behaviors of the male peacocks. The first male peacock has the highest fitness, and the fitness of the second male peacocks except the first male peacocks is decreased gradually. The higher the fitness of the male peacock, the higher the probability that the male peacock rotates around the food source, and the smaller the radius of the male peacock rotating around the food source, the smaller the value of the third position updating operator; conversely, the more the male peacock tends to rotate in place, and the larger the radius of rotation of the male peacock about the food source, the greater the value of the third location update operator. Meanwhile, the larger the value of the third position updating operator is, the higher the possibility that the random number falls into the range is, so that the influence of the position of the first male peacock on the positions of other peacocks is the largest, and the influence of the positions of the other peacocks by the second male peacocks except the first male peacocks is decreased progressively in sequence.

In one embodiment, as shown in fig. 8, the assigning a role to each peacock according to the fitness comprises:

s501, sorting the peacocks according to the fitness.

Before iteration, sequencing each peacock according to the initial fitness; and in the iteration process, sequencing each peacock according to the new fitness calculated by each iteration.

And S502, distributing the N peacocks with high fitness as the male peacocks.

For example, the first 5 peacocks with high fitness are assigned to male peacocks.

And S503, distributing the non-male peacocks with high fitness according to the preset proportion into female peacocks.

For example, if the predetermined ratio is 30%, then the first 30% of the remaining peacocks, excluding the male peacocks, will be female peacocks.

And S504, distributing the rest peacocks into young peacocks.

For example, the first 5 peacocks with high fitness are assigned to be male peacocks, the remaining first 30% are female peacocks, and the remainder are young peacocks.

In the above embodiment, the peacocks are sorted according to the fitness, the N peacocks with high fitness are allocated as male peacocks, the non-male peacocks with high fitness in the preset proportion are allocated as female peacocks, and the rest peacocks are allocated as young peacocks. The peacock population is divided by sequencing the high and low fitness, and a foundation is laid for the mutual matching of position updating mechanisms among later male peacocks, female peacocks and young peacocks.

In one embodiment, as shown in fig. 9, a process for identifying the internal resistance of the storage battery is provided, which is described by taking a server as an example, and includes the following steps:

s601, establishing an equivalent circuit model of the storage battery to obtain a plurality of internal resistance parameters to be identified.

And S602, determining a fitness function and a constraint condition.

The user can determine the calculated value voltage u by combining the peacock optimization algorithm with the formula (2) _L The measured value voltage is acquired through constant-current voltage-limiting charging and checking discharge test acquisition

The fitness is thus calculated with the aid of MATLAB software according to equation (3). Furthermore, the constraint conditions may also be set by MATLAB software.

And S603, initializing the position and fitness according to a peacock optimization algorithm.

According to the peacock optimization algorithm, the initial position of each peacock can be set as a search space, specifically, the search space is an 8-dimensional space formed by the value range of the formula (4), and the initial position of each peacock is a random position in the 8-dimensional space. The initialization of fitness is the result of this random position calculated by equation (3).

And S604, allocating roles to each peacock according to the fitness.

Sequencing the peacocks according to the fitness, distributing N peacocks with high fitness as male peacocks, distributing non-male peacocks with high fitness in a preset proportion as female peacocks, and distributing the rest peacocks as young peacocks.

And S605, updating the positions of the peacocks based on the self-adaptive behavior of the peacocks in the iterative calculation process of each time.

The self-adaptation through female peacock is close the position that male peacock action updated each female peacock, and the self-adaptation search action through young peacock updates the position of each young peacock, and the mutual action through between the male peacock updates the position of the male peacock of second except that first male peacock, and first male peacock is the male peacock of the highest fitness.

And S606, recalculating the fitness according to the updated positions of the peacocks.

Substituting the positions of the peacocks into an equivalent model circuit formula (1) and a formula (2) according to the updated positions of the peacocks to calculate a calculated value voltage u _L And substituting the fitness function formula (3) to obtain a new fitness value of each peacock. The better the fitness of each peacock, the more accurate the internal resistance parameter of the storage battery to be distinguished.

And S607, reallocating the roles according to the recalculated fitness and performing iterative computation again according to the reallocated roles.

And S608, obtaining the optimal solution of a plurality of internal resistance parameters according to the final position of each peacock after the iteration is finished.

And when the iteration is finished, the final position of each peacock corresponds to the position with the best fitness, namely the optimal solution of a plurality of internal resistance parameters.

In the embodiment, an equivalent circuit model of the storage battery is established to obtain a plurality of internal resistance parameters to be identified; determining a fitness function and a constraint condition; initializing the position and fitness according to a peacock optimization algorithm; allocating roles to each peacock according to the fitness; in each iterative calculation process, updating the positions of the peacocks based on the self-adaptive behaviors of the peacocks; recalculating the fitness according to the updated position of each peacock; re-distributing roles according to the re-calculated fitness, and performing iterative calculation again according to the re-distributed roles; and when the iteration is finished, obtaining the optimal solution of a plurality of internal resistance parameters according to the final position of each peacock. The comprehensive state in the storage battery is a fundamental factor for determining the health condition of the storage battery, and any change in the storage battery can cause the change of the internal resistance parameter of the storage battery, so the internal resistance parameter of the storage battery is a characteristic quantity capable of reflecting the health condition of the storage battery; meanwhile, the internal resistance parameter of the storage battery is difficult to measure due to the influence of the dynamic change characteristics of the chemical reaction in the storage battery, so the accuracy of the internal resistance parameter measurement is very important in the storage battery monitoring technology. By the embodiment of the application, the internal resistance parameters of the storage battery can be acquired more quickly and accurately, the change of the health condition of the storage battery can be predicted, operation and maintenance personnel in a transformer substation and a converter station can find the storage battery with reduced performance in time, and can maintain or replace the storage battery in time, so that a power grid is prevented from generating serious operation accidents.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.

Based on the same inventive concept, the embodiment of the application also provides a storage battery internal resistance identification device for realizing the storage battery internal resistance identification method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so that specific limitations in one or more embodiments of the storage battery internal resistance identification device provided below can be referred to the limitations on the storage battery internal resistance identification method in the foregoing, and details are not repeated herein.

In one embodiment, as shown in fig. 10, there is provided a storage battery internal resistance recognition device including:

the model establishing module 701 is used for establishing an equivalent circuit model of the storage battery to obtain a plurality of internal resistance parameters to be identified;

a function determination module 702 for determining a fitness function and a constraint condition; the fitness function comprises a deviation relation between a measured value and a calculated value of the internal resistance parameter, and the constraint condition comprises a variation range of each internal resistance parameter;

the parameter solving module 703 is configured to solve the plurality of internal resistance parameters according to a peacock optimization algorithm, a fitness function, and a constraint condition, so as to obtain an optimal solution of the plurality of internal resistance parameters.

In one embodiment, as shown in fig. 11, the parameter solving module 703 includes:

the initialization submodule 7031 is configured to initialize the position and fitness according to a peacock optimization algorithm; the position of each peacock corresponds to a group of solutions of the internal resistance parameters, and the fitness of each peacock represents the deviation degree between the group of solutions of the internal resistance parameters and the measured value;

a role assignment submodule 7032 configured to assign a role to each peacock according to the fitness; the characters include male peacocks, female peacocks and young peacocks;

and the iterative computation submodule 7033 is configured to perform iterative computation on the plurality of internal resistance parameters for multiple times according to the role, the fitness function, and the constraint condition, so as to obtain an optimal solution of the plurality of internal resistance parameters.

In one embodiment, the iterative computation submodule 7033 includes:

the position updating unit is used for updating the positions of the peacocks based on the self-adaptive behaviors of the peacocks in each iterative calculation process;

the fitness calculating unit is used for recalculating the fitness according to the updated position of each peacock;

the iterative calculation unit is used for redistributing the roles according to the recalculated fitness and carrying out iterative calculation again according to the redistributed roles;

and the optimal solution obtaining unit is used for obtaining the optimal solution of the internal resistance parameters according to the final position of each peacock when the iteration is finished.

In one embodiment, the position updating unit is specifically configured to update the position of each female peacock by the adaptive approach of the female peacock to the behavior of the male peacock; updating the positions of all the young peacocks through the self-adaptive search behavior of the young peacocks; updating the positions of second male peacocks except the first male peacocks through interaction behaviors among the male peacocks; the first male peacock is the one with the highest fitness.

In one embodiment, the position updating unit is specifically configured to obtain a position of a female peacock, a current position of a male peacock, a first random number, a first position updating operator, and a first search operator after iteration; and calculating the position of the iterated female peacock according to a preset female peacock behavior description formula, the position of the iterated female peacock, the position of the current male peacock, a first random number, a first position update operator and a first search operator.

In one embodiment, the position updating unit is specifically configured to obtain a position of a young peacock after iteration, a position of a current young peacock, a flight factor, a position of a male peacock followed by the young peacock, a position of the current male peacock, a second random number, a second position updating operator, and a dynamic change operator; and calculating the position of the young peacock after iteration according to a preset young peacock behavior description formula, the position of the young peacock after iteration, the position of the current young peacock, a flight factor, the position of the male peacock followed by the young peacock, the position of the current male peacock, a second random number, a second position updating operator and a dynamic change operator.

In one embodiment, the position updating unit is specifically configured to obtain a random vector, a current position of a male peacock, a third search operator, and a third random number; and calculating the position of a second male peacock according to a preset male peacock interaction behavior description formula, a random vector, the current position of the male peacock, a third search operator and a third random number.

In one embodiment, the position updating unit is further configured to dynamically adjust the position of the male peacock based on the duality of the male peacock.

In one embodiment, the role assignment sub-module 7032 is specifically configured to rank the peacocks according to the fitness; distributing the N peacocks with high fitness as male peacocks; distributing the non-male peacocks with high fitness in a preset proportion into female peacocks; and distributing the rest peacocks as young peacocks.

All or part of each module in the storage battery internal resistance identification device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 12. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the identification data of the internal resistance of the storage battery. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a battery internal resistance identification method.

It will be appreciated by those skilled in the art that the configuration shown in fig. 12 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:

In one embodiment, the processor, when executing the computer program, further performs the steps of:

initializing the position and fitness according to a peacock optimization algorithm; the position of each peacock corresponds to a group of solutions of the internal resistance parameters, and the fitness of each peacock represents the deviation degree between the group of solutions of the internal resistance parameters and the measured value;

allocating roles to each peacock according to the fitness; the characters include male peacocks, female peacocks and young peacocks;

and carrying out repeated iterative calculation on the plurality of internal resistance parameters according to the roles, the fitness function and the constraint conditions to obtain the optimal solution of the plurality of internal resistance parameters.

In one embodiment, the processor when executing the computer program further performs the steps of:

in each iterative calculation process, updating the positions of the peacocks based on the self-adaptive behaviors of the peacocks;

recalculating the fitness according to the updated positions of the peacocks;

reallocating roles according to the recalculated fitness, and performing iterative computation again according to the reallocating roles;

and when the iteration is finished, obtaining the optimal solution of a plurality of internal resistance parameters according to the final position of each peacock.

updating the positions of the female peacocks by the self-adaption of the female peacocks approaching to the behavior of the male peacocks;

updating the positions of all the young peacocks through the self-adaptive search behavior of the young peacocks;

updating the positions of second male peacocks except the first male peacocks through interaction behaviors among the male peacocks; the first male peacock is the one with the highest fitness.

obtaining the position of a female peacock, the current position of the female peacock, the current position of a male peacock, a first random number, a first position updating operator and a first searching operator after iteration;

and calculating the position of the iterated female peacock according to a preset female peacock behavior description formula, the position of the iterated female peacock, the position of the current male peacock, a first random number, a first position update operator and a first search operator.

acquiring the position of a young peacock, the position of a current young peacock, a flight factor, the position of a male peacock followed by the young peacock, the position of the current male peacock, a second random number, a second position updating operator and a dynamic change operator after iteration;

and calculating the position of the young peacock after iteration according to a preset young peacock behavior description formula, the position of the young peacock after iteration, the position of the current young peacock, a flight factor, the position of the male peacock followed by the young peacock, the position of the current male peacock, a second random number, a second position updating operator and a dynamic change operator.

acquiring a random vector, the current position of the male peacock, a third search operator and a third random number;

and calculating the position of a second male peacock according to a preset male peacock interaction behavior description formula, a random vector, the current position of the male peacock, a third search operator and a third random number.

and dynamically adjusting the position of the male peacock based on the duality behavior of the male peacock.

sequencing each peacock according to the fitness;

distributing the N peacocks with high fitness as male peacocks;

allocating the non-male peacocks with high fitness in a preset proportion as female peacocks;

and distributing the rest peacocks as young peacocks.

In an exemplary embodiment, a storage medium comprising instructions, such as a memory comprising instructions, executable by a processor of a server to perform the above method is also provided. The storage medium may be a non-transitory computer readable storage medium, which may be, for example, a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

In an exemplary embodiment, a computer program product is also provided, which, when executed by a processor, may carry out the above-mentioned method. The computer program product includes one or more computer instructions. When loaded and executed on a computer, may implement some or all of the above-described methods, in whole or in part, according to the procedures or functions described in the embodiments of the disclosure.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, databases, or other media used in the embodiments provided herein can include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the various embodiments provided herein may be, without limitation, general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, or the like.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A storage battery internal resistance identification method is characterized by comprising the following steps:

and solving the plurality of internal resistance parameters according to a peacock optimization algorithm, the fitness function and the constraint condition to obtain an optimal solution of the plurality of internal resistance parameters.

2. The method of claim 1, wherein solving the plurality of internal resistance parameters according to a peacock optimization algorithm, the fitness function, and the constraint condition to obtain an optimal solution of the plurality of internal resistance parameters comprises:

allocating roles to each peacock according to the fitness; the characters include male, female and young peacocks;

and carrying out repeated iterative computation on the internal resistance parameters according to the roles, the fitness function and the constraint conditions to obtain the optimal solution of the internal resistance parameters.

3. The method of claim 2, wherein the iteratively calculating the plurality of internal resistance parameters a plurality of times according to the role, the fitness function, and the constraint condition to obtain an optimal solution of the plurality of internal resistance parameters comprises:

updating the position of each of the peacocks based on the adaptive behavior of each of the peacocks during each iterative computation;

recalculating the fitness according to the updated positions of the peacocks;

re-distributing roles according to the re-calculated fitness, and performing iterative calculation again according to the re-distributed roles;

and when the iteration is finished, obtaining the optimal solution of the internal resistance parameters according to the final position of each peacock.

4. The method of claim 3, wherein updating the position of each of the peacocks based on the adaptive behavior of each of the peacocks comprises:

updating the position of each female peacock through the self-adaptive approaching of the female peacocks to the behavior of the male peacocks;

updating the positions of the young peacocks through the self-adaptive search behavior of the young peacocks;

updating the position of a second male peacock, other than the first, by the interaction behavior between the male peacocks; the first male peacock is the male peacock with the highest fitness.

5. The method of claim 4, wherein said updating the position of each of said female peacocks by their adaptive approach to male peacock behavior comprises:

acquiring the position of a female peacock, the current position of the female peacock, the current position of a male peacock, a first random number, a first position updating operator and a first searching operator after iteration;

and calculating the position of the iterated female peacock according to a preset female peacock behavior description formula, the position of the iterated female peacock, the position of the current male peacock, the first random number, the first position updating operator and the first searching operator.

6. The method according to claim 4, wherein the updating of the position of each of the young peacocks through adaptive search behavior of the young peacocks comprises:

according to a preset young peacock behavior description formula, the position of the young peacock after iteration, the position of the current young peacock, the flight factor, the position of the male peacock followed by the young peacock, the position of the current male peacock, the second random number, the second position update operator and the dynamic change operator, the position of the young peacock after iteration is calculated.

7. An apparatus for recognizing an internal resistance of a secondary battery, the apparatus comprising:

and the parameter solving module is used for solving the plurality of internal resistance parameters according to a peacock optimization algorithm, the fitness function and the constraint condition to obtain an optimal solution of the plurality of internal resistance parameters.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 9 when executing the computer program.

9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 9.

10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 9 when executed by a processor.